Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California, USA.
Bioinformatics Interdepartmental Graduate Program, University of California at Los Angeles, Los Angeles, California, USA.
J Comput Biol. 2022 Aug;29(8):769-781. doi: 10.1089/cmb.2022.0002. Epub 2022 Jun 6.
Developing cancer prognostic models using multiomics data is a major goal of precision oncology. DNA methylation provides promising prognostic biomarkers, which have been used to predict survival and treatment response in solid tumor or plasma samples. This review article presents an overview of recently published computational analyses on DNA methylation for cancer prognosis. To address the challenges of survival analysis with high-dimensional methylation data, various feature selection methods have been applied to screen a subset of informative markers. Using candidate markers associated with survival, prognostic models either predict risk scores or stratify patients into subtypes. The model's discriminatory power can be assessed by multiple evaluation metrics. Finally, we discuss the limitations of existing studies and present the prospects of applying machine learning algorithms to fully exploit the prognostic value of DNA methylation.
利用多组学数据开发癌症预后模型是精准肿瘤学的主要目标。DNA 甲基化提供了有前途的预后生物标志物，已被用于预测实体瘤或血浆样本中的生存和治疗反应。本文综述了最近发表的关于 DNA 甲基化用于癌症预后的计算分析。为了解决高维甲基化数据生存分析的挑战，已经应用了各种特征选择方法来筛选一组有意义的标记物。使用与生存相关的候选标记物，预后模型要么预测风险评分，要么将患者分为亚型。可以通过多种评估指标来评估模型的判别能力。最后，我们讨论了现有研究的局限性，并提出了应用机器学习算法充分利用 DNA 甲基化预后价值的前景。
